"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class   __UpperCAmelCase		:

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										return None






class   __UpperCAmelCase		:

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										return None






class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  int    				=			[
				    # (model_name, model_kwargs)
				    ('''bert-base-cased''', {}),
				    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
				]

				@require_tf
				@slow
				def 						_a      ( self ):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																self._test_export(_lowerCamelCase      ,					"tf"      ,					12      ,					**_lowerCamelCase )

				@require_torch
				@slow
				def 						_a      ( self ):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																self._test_export(_lowerCamelCase      ,					"pt"      ,					12      ,					**_lowerCamelCase )

				@require_torch
				@slow
				def 						_a      ( self ):
										from transformers import BertModel

										lowerCamelCase__   =["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t" ) as vocab_file:
																vocab_file.write("\n".join(_lowerCamelCase ) )
																vocab_file.flush()
																lowerCamelCase__   =BertTokenizerFast(vocab_file.name )

										with TemporaryDirectory() as bert_save_dir:
																lowerCamelCase__   =BertModel(BertConfig(vocab_size=len(_lowerCamelCase ) ) )
																model.save_pretrained(_lowerCamelCase )
																self._test_export(_lowerCamelCase      ,					"pt"      ,					12      ,					_lowerCamelCase )

				@require_tf
				@slow
				def 						_a      ( self ):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																lowerCamelCase__   =self._test_export(_lowerCamelCase      ,					"tf"      ,					12      ,					**_lowerCamelCase )
																lowerCamelCase__   =quantize(Path(_lowerCamelCase ) )

																# Ensure the actual quantized model is not bigger than the original one
																if quantized_path.stat().st_size >= Path(_lowerCamelCase ).stat().st_size:
																						self.fail("Quantized model is bigger than initial ONNX model" )

				@require_torch
				@slow
				def 						_a      ( self ):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																lowerCamelCase__   =self._test_export(_lowerCamelCase      ,					"pt"      ,					12      ,					**_lowerCamelCase )
																lowerCamelCase__   =quantize(_lowerCamelCase )

																# Ensure the actual quantized model is not bigger than the original one
																if quantized_path.stat().st_size >= Path(_lowerCamelCase ).stat().st_size:
																						self.fail("Quantized model is bigger than initial ONNX model" )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					**_lowerCamelCase ):
										try:
																# Compute path
																with TemporaryDirectory() as tempdir:
																						lowerCamelCase__   =Path(_lowerCamelCase ).joinpath("model.onnx" )

																# Remove folder if exists
																if path.parent.exists():
																						path.parent.rmdir()

																# Export
																convert(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					**_lowerCamelCase )

																return path
										except Exception as e:
																self.fail(_lowerCamelCase )

				@require_torch
				@require_tokenizers
				@slow
				def 						_a      ( self ):
										from transformers import BertModel

										lowerCamelCase__   =BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
										lowerCamelCase__   =BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
										self._test_infer_dynamic_axis(_lowerCamelCase      ,					_lowerCamelCase      ,					"pt" )

				@require_tf
				@require_tokenizers
				@slow
				def 						_a      ( self ):
										from transformers import TFBertModel

										lowerCamelCase__   =TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
										lowerCamelCase__   =BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
										self._test_infer_dynamic_axis(_lowerCamelCase      ,					_lowerCamelCase      ,					"tf" )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =FeatureExtractionPipeline(_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   =["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =infer_shapes(_lowerCamelCase      ,					_lowerCamelCase )

										# Assert all variables are present
										self.assertEqual(len(_lowerCamelCase )      ,					len(_lowerCamelCase ) )
										self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
										self.assertSequenceEqual(variable_names[:3]      ,					_lowerCamelCase )
										self.assertSequenceEqual(variable_names[3:]      ,					_lowerCamelCase )

										# Assert inputs are {0: batch, 1: sequence}
										for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																self.assertDictEqual(shapes[var_name]      ,					{0: "batch", 1: "sequence"} )

										# Assert outputs are {0: batch, 1: sequence} and {0: batch}
										self.assertDictEqual(shapes["output_0"]      ,					{0: "batch", 1: "sequence"} )
										self.assertDictEqual(shapes["output_1"]      ,					{0: "batch"} )

				def 						_a      ( self ):
										lowerCamelCase__   =["input_ids", "attention_mask", "token_type_ids"]
										lowerCamelCase__   ={"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										lowerCamelCase__   , lowerCamelCase__   =ensure_valid_input(FuncContiguousArgs()      ,					_lowerCamelCase      ,					_lowerCamelCase )

										# Should have exactly the same number of args (all are valid)
										self.assertEqual(len(_lowerCamelCase )      ,					3 )

										# Should have exactly the same input names
										self.assertEqual(set(_lowerCamelCase )      ,					set(_lowerCamelCase ) )

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(_lowerCamelCase      ,					(tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										lowerCamelCase__   , lowerCamelCase__   =ensure_valid_input(FuncNonContiguousArgs()      ,					_lowerCamelCase      ,					_lowerCamelCase )

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(_lowerCamelCase )      ,					1 )
										self.assertEqual(len(_lowerCamelCase )      ,					1 )

										# Should have only "input_ids"
										self.assertEqual(inputs_args[0]      ,					tokens["input_ids"] )
										self.assertEqual(ordered_input_names[0]      ,					"input_ids" )






				def 						_a      ( self ):
										lowerCamelCase__   =generate_identified_filename(Path("/home/something/my_fake_model.onnx" )      ,					"-test" )
										self.assertEqual("/home/something/my_fake_model-test.onnx"      ,					generated.as_posix() )



"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	a				=None

a				=logging.get_logger(__name__)
a				={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a				={
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

a				={
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

a				='▁'






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Dict    				=			VOCAB_FILES_NAMES
				A__ :  List[Any]    				=			PRETRAINED_VOCAB_FILES_MAP
				A__ :  List[Any]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__ :  Union[str, Any]    				=			AlbertTokenizer

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<unk>"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<pad>"      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[MASK]"      ,					**_lowerCamelCase      ,					):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowerCamelCase__   =(
										    AddedToken(_lowerCamelCase      ,					lstrip=_lowerCamelCase      ,					rstrip=_lowerCamelCase      ,					normalized=_lowerCamelCase )
										    if isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										    else mask_token
										)

										super().__init__(
										    _lowerCamelCase      ,					tokenizer_file=_lowerCamelCase      ,					do_lower_case=_lowerCamelCase      ,					remove_space=_lowerCamelCase      ,					keep_accents=_lowerCamelCase      ,					bos_token=_lowerCamelCase      ,					eos_token=_lowerCamelCase      ,					unk_token=_lowerCamelCase      ,					sep_token=_lowerCamelCase      ,					pad_token=_lowerCamelCase      ,					cls_token=_lowerCamelCase      ,					mask_token=_lowerCamelCase      ,					**_lowerCamelCase      ,					)

										lowerCamelCase__   =do_lower_case
										lowerCamelCase__   =remove_space
										lowerCamelCase__   =keep_accents
										lowerCamelCase__   =vocab_file
										lowerCamelCase__   =False if not self.vocab_file else True

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																    "tokenizer." )

										if not os.path.isdir(_lowerCamelCase ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																return
										lowerCamelCase__   =os.path.join(
										    _lowerCamelCase      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

										if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
																copyfile(self.vocab_file      ,					_lowerCamelCase )

										return (out_vocab_file,)


